Changed the parameter's decription where relevant as per the issue 2177

src/Microsoft.ML.Data/Transforms/GenerateNumberTransform.cs

@@ -266,7 +266,7 @@ private static VersionInfo GetVersionInfo()
         /// </summary>
         /// <param name="env">Host Environment.</param>
         /// <param name="input">Input <see cref="IDataView"/>. This is the output from previous transform or loader.</param>
-        /// <param name="name">Name of the output column.</param>
+        /// <param name="name">The name of the output column.</param>
         /// <param name="seed">Seed to start random number generator.</param>
         /// <param name="useCounter">Use an auto-incremented integer starting at zero instead of a random number.</param>
         public GenerateNumberTransform(IHostEnvironment env, IDataView input, string name, uint? seed = null, bool useCounter = Defaults.UseCounter)

src/Microsoft.ML.Data/Transforms/LabelConvertTransform.cs

@@ -72,8 +72,8 @@ private static VersionInfo GetVersionInfo()
         /// </summary>
         /// <param name="env">Host Environment.</param>
         /// <param name="input">Input <see cref="IDataView"/>. This is the output from previous transform or loader.</param>
-        /// <param name="outputColumnName">Name of the output column.</param>
-        /// <param name="inputColumnName">Name of the input column.  If this is null '<paramref name="outputColumnName"/>' will be used.</param>
+        /// <param name="outputColumnName">The name of the output column.</param>
+        /// <param name="inputColumnName">The name of the input column.  If this is null '<paramref name="outputColumnName"/>' will be used.</param>
         public LabelConvertTransform(IHostEnvironment env, IDataView input, string outputColumnName, string inputColumnName = null)
             : this(env, new Arguments() { Columns = new[] { new Column() { Source = inputColumnName ?? outputColumnName, Name = outputColumnName } } }, input)
         {

src/Microsoft.ML.Data/Transforms/LabelIndicatorTransform.cs

@@ -120,8 +120,8 @@ private static string TestIsMulticlassLabel(ColumnType type)
         /// <param name="env">Host Environment.</param>
         /// <param name="input">Input <see cref="IDataView"/>. This is the output from previous transform or loader.</param>
         /// <param name="classIndex">Label of the positive class.</param>
-        /// <param name="name">Name of the output column.</param>
-        /// <param name="source">Name of the input column.  If this is null '<paramref name="name"/>' will be used.</param>
+        /// <param name="name">The name of the output column.</param>
+        /// <param name="source">The name of the input column.  If this is null '<paramref name="name"/>' will be used.</param>
         public LabelIndicatorTransform(IHostEnvironment env,
             IDataView input,
             int classIndex,

src/Microsoft.ML.Data/Transforms/NormalizeColumn.cs

@@ -269,8 +269,8 @@ public sealed class SupervisedBinArguments : BinArgumentsBase
         /// </summary>
         /// <param name="env">Host Environment.</param>
         /// <param name="input">Input <see cref="IDataView"/>. This is the output from previous transform or loader.</param>
-        /// <param name="outputColumnName">Name of the output column.</param>
-        /// <param name="inputColumnName">Name of the column to be transformed. If this is null '<paramref name="outputColumnName"/>' will be used.</param>
+        /// <param name="outputColumnName">The name of the output column.</param>
+        /// <param name="inputColumnName">The name of the column to be transformed. If this is null '<paramref name="outputColumnName"/>' will be used.</param>
         public static IDataView CreateMinMaxNormalizer(IHostEnvironment env, IDataView input, string outputColumnName, string inputColumnName = null)
         {
             Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.Data/Transforms/RangeFilter.cs

@@ -86,7 +86,7 @@ private static VersionInfo GetVersionInfo()
         /// </summary>
         /// <param name="env">Host Environment.</param>
         /// <param name="input">Input <see cref="IDataView"/>. This is the output from previous transform or loader.</param>
-        /// <param name="column">Name of the input column.</param>
+        /// <param name="column">The name of the input column.</param>
         /// <param name="lowerBound">Minimum value (0 to 1 for key types).</param>
         /// <param name="upperBound">Maximum value (0 to 1 for key types).</param>
         /// <param name="includeUpperBound">Whether to include the upper bound.</param>

src/Microsoft.ML.Data/Transforms/TypeConverting.cs

@@ -208,8 +208,8 @@ private static (string outputColumnName, string inputColumnName)[] GetColumnPair
         /// Convinence constructor for simple one column case.
         /// </summary>
         /// <param name="env">Host Environment.</param>
-        /// <param name="outputColumnName">Name of the output column.</param>
-        /// <param name="inputColumnName">Name of the column to be transformed. If this is null '<paramref name="outputColumnName"/>' will be used.</param>
+        /// <param name="outputColumnName">The name of the output column.</param>
+        /// <param name="inputColumnName">The name of the column to be transformed. If this is null '<paramref name="outputColumnName"/>' will be used.</param>
         /// <param name="outputKind">The expected type of the converted column.</param>
         /// <param name="outputKeyCount">New key count if we work with key type.</param>
         public TypeConvertingTransformer(IHostEnvironment env, string outputColumnName, DataKind outputKind, string inputColumnName = null, KeyCount outputKeyCount = null)

src/Microsoft.ML.FastTree/TreeTrainersCatalog.cs

@@ -57,9 +57,9 @@ public static FastTreeRegressionTrainer FastTree(this RegressionCatalog.Regressi
         /// Predict a target using a decision tree binary classification model trained with the <see cref="FastTreeBinaryClassificationTrainer"/>.
         /// </summary>
         /// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
-        /// <param name="featureColumn">The featureColumn column.</param>
-        /// <param name="weights">The optional weights column.</param>
+        /// <param name="labelColumn">The name of the label column.</param>
+        /// <param name="featureColumn">The name of the feature column.</param>
+        /// <param name="weights">The name of the optional weights column.</param>
         /// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
         /// <param name="numLeaves">The maximum number of leaves per decision tree.</param>
         /// <param name="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param>
@@ -97,10 +97,10 @@ public static FastTreeBinaryClassificationTrainer FastTree(this BinaryClassifica
         /// Ranks a series of inputs based on their relevance, training a decision tree ranking model through the <see cref="FastTreeRankingTrainer"/>.
         /// </summary>
         /// <param name="catalog">The <see cref="RankingCatalog"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
-        /// <param name="featureColumn">The featureColumn column.</param>
+        /// <param name="labelColumn">The name of the label column.</param>
+        /// <param name="featureColumn">The name of the feature column.</param>
         /// <param name="groupId">The groupId column.</param>
-        /// <param name="weights">The optional weights column.</param>
+        /// <param name="weights">The name of the optional weights column.</param>
         /// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
         /// <param name="numLeaves">The maximum number of leaves per decision tree.</param>
         /// <param name="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param>
@@ -139,9 +139,9 @@ public static FastTreeRankingTrainer FastTree(this RankingCatalog.RankingTrainer
         /// Predict a target using generalized additive models trained with the <see cref="BinaryClassificationGamTrainer"/>.
         /// </summary>
         /// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
-        /// <param name="featureColumn">The featureColumn column.</param>
-        /// <param name="weights">The optional weights column.</param>
+        /// <param name="labelColumn">The name of the label column.</param>
+        /// <param name="featureColumn">The name of the feature column.</param>
+        /// <param name="weights">The name of the optional weights column.</param>
         /// <param name="numIterations">The number of iterations to use in learning the features.</param>
         /// <param name="learningRate">The learning rate. GAMs work best with a small learning rate.</param>
         /// <param name="maxBins">The maximum number of bins to use to approximate features.</param>
@@ -175,9 +175,9 @@ public static BinaryClassificationGamTrainer GeneralizedAdditiveModels(this Bina
         /// Predict a target using generalized additive models trained with the <see cref="RegressionGamTrainer"/>.
         /// </summary>
         /// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
-        /// <param name="featureColumn">The featureColumn column.</param>
-        /// <param name="weights">The optional weights column.</param>
+        /// <param name="labelColumn">The name of the label column.</param>
+        /// <param name="featureColumn">The name of the feature column.</param>
+        /// <param name="weights">The name of the optional weights column.</param>
         /// <param name="numIterations">The number of iterations to use in learning the features.</param>
         /// <param name="learningRate">The learning rate. GAMs work best with a small learning rate.</param>
         /// <param name="maxBins">The maximum number of bins to use to approximate features.</param>
@@ -211,9 +211,9 @@ public static RegressionGamTrainer GeneralizedAdditiveModels(this RegressionCata
         /// Predict a target using a decision tree regression model trained with the <see cref="FastTreeTweedieTrainer"/>.
         /// </summary>
         /// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
-        /// <param name="featureColumn">The featureColumn column.</param>
-        /// <param name="weights">The optional weights column.</param>
+        /// <param name="labelColumn">The name of the label column.</param>
+        /// <param name="featureColumn">The name of the feature column.</param>
+        /// <param name="weights">The name of the optional weights column.</param>
         /// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
         /// <param name="numLeaves">The maximum number of leaves per decision tree.</param>
         /// <param name="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param>
@@ -251,9 +251,9 @@ public static FastTreeTweedieTrainer FastTreeTweedie(this RegressionCatalog.Regr
         /// Predict a target using a decision tree regression model trained with the <see cref="FastForestRegression"/>.
         /// </summary>
         /// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
-        /// <param name="featureColumn">The featureColumn column.</param>
-        /// <param name="weights">The optional weights column.</param>
+        /// <param name="labelColumn">The name of the label column.</param>
+        /// <param name="featureColumn">The name of the feature column.</param>
+        /// <param name="weights">The name of the optional weights column.</param>
         /// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
         /// <param name="numLeaves">The maximum number of leaves per decision tree.</param>
         /// <param name="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param>
@@ -291,9 +291,9 @@ public static FastForestRegression FastForest(this RegressionCatalog.RegressionT
         /// Predict a target using a decision tree regression model trained with the <see cref="FastForestClassification"/>.
         /// </summary>
         /// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
-        /// <param name="featureColumn">The featureColumn column.</param>
-        /// <param name="weights">The optional weights column.</param>
+        /// <param name="labelColumn">The name of the label column.</param>
+        /// <param name="featureColumn">The name of the feature column.</param>
+        /// <param name="weights">The name of the optional weights column.</param>
         /// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
         /// <param name="numLeaves">The maximum number of leaves per decision tree.</param>
         /// <param name="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param>

src/Microsoft.ML.HalLearners/HalLearnersCatalog.cs

@@ -20,9 +20,9 @@ public static class HalLearnersCatalog
         /// Predict a target using a linear regression model trained with the <see cref="OlsLinearRegressionTrainer"/>.
         /// </summary>
         /// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
-        /// <param name="featureColumn">The features column.</param>
-        /// <param name="weights">The weights column.</param>
+        /// <param name="labelColumn">The name of the label column.</param>
+        /// <param name="featureColumn">The name of the features column.</param>
+        /// <param name="weights">The name of the weights column.</param>
         public static OlsLinearRegressionTrainer OrdinaryLeastSquares(this RegressionCatalog.RegressionTrainers catalog,
             string labelColumn = DefaultColumnNames.Label,
             string featureColumn = DefaultColumnNames.Features,
@@ -60,8 +60,8 @@ public static OlsLinearRegressionTrainer OrdinaryLeastSquares(
         ///  Predict a target using a linear binary classification model trained with the <see cref="SymSgdClassificationTrainer"/>.
         /// </summary>
         /// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
-        /// <param name="featureColumn">The features column.</param>
+        /// <param name="labelColumn">The name of the label column.</param>
+        /// <param name="featureColumn">The name of the features column.</param>
         public static SymSgdClassificationTrainer SymbolicStochasticGradientDescent(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
             string labelColumn = DefaultColumnNames.Label,
             string featureColumn = DefaultColumnNames.Features)

src/Microsoft.ML.ImageAnalytics/ExtensionsCatalog.cs

@@ -89,8 +89,8 @@ public static ImagePixelExtractingEstimator ExtractPixels(this TransformsCatalog
         /// <seealso cref= "ImageLoadingEstimator" />
         /// </remarks >
         /// <param name="catalog">The transform's catalog.</param>
-        /// <param name="inputColumnName">Name of the input column.</param>
-        /// <param name="outputColumnName">Name of the resulting output column.</param>
+        /// <param name="inputColumnName">The name of the input column.</param>
+        /// <param name="outputColumnName">The name of the resulting output column.</param>
         /// <param name="imageWidth">The transformed image width.</param>
         /// <param name="imageHeight">The transformed image height.</param>
         /// <param name="resizing"> The type of image resizing as specified in <see cref="ImageResizingEstimator.ResizingKind"/>.</param>

src/Microsoft.ML.ImageAnalytics/ImagePixelExtractor.cs

@@ -700,7 +700,7 @@ internal void Save(ModelSaveContext ctx)
         ///</summary>
         /// <param name="env">The host environment.</param>
         /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>. Null means <paramref name="inputColumnName"/> is replaced.</param>
-        /// <param name="inputColumnName">Name of the input column.</param>
+        /// <param name="inputColumnName">The name of the input column.</param>
         /// <param name="colors">What colors to extract.</param>
         /// <param name="interleave">Whether to interleave the pixels, meaning keep them in the `RGB RGB` order, or leave them in the plannar form: of all red pixels,
         /// than all green, than all blue.</param>

src/Microsoft.ML.ImageAnalytics/ImageResizer.cs

@@ -135,7 +135,7 @@ private static VersionInfo GetVersionInfo()
         /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
         /// <param name="imageWidth">Width of resized image.</param>
         /// <param name="imageHeight">Height of resized image.</param>
-        /// <param name="inputColumnName">Name of the input column.</param>
+        /// <param name="inputColumnName">The name of the input column.</param>
         /// <param name="resizing">What <see cref="ImageResizingEstimator.ResizingKind"/> to use.</param>
         /// <param name="cropAnchor">If <paramref name="resizing"/> set to <see cref="ImageResizingEstimator.ResizingKind.IsoCrop"/> what anchor to use for cropping.</param>
         internal ImageResizingTransformer(IHostEnvironment env, string outputColumnName,
@@ -524,7 +524,7 @@ public ColumnInfo(string name,
         /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
         /// <param name="imageWidth">Width of resized image.</param>
         /// <param name="imageHeight">Height of resized image.</param>
-        /// <param name="inputColumnName">Name of the input column.</param>
+        /// <param name="inputColumnName">The name of the input column.</param>
         /// <param name="resizing">What <see cref="ResizingKind"/> to use.</param>
         /// <param name="cropAnchor">If <paramref name="resizing"/> set to <see cref="ResizingKind.IsoCrop"/> what anchor to use for cropping.</param>
         internal ImageResizingEstimator(IHostEnvironment env,